Differential phospho analysis - CRC Batch 2 Set 1
Sophie Herbst
Analysis date: 2023-08-08
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")TODO
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set1_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.7889344
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.7029126
## 3: ABC transporter disorders 0.5264484
## 4: ABC-family proteins mediated transport 0.5264484
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.4951456
## 6: ADP signalling through P2Y purinoceptor 1 0.5532995
## padj log2err ES NES size
## 1: 0.9690732 0.05773085 0.6088710 0.8140883 1
## 2: 0.9653366 0.06064040 -0.6431452 -0.8637563 1
## 3: 0.9653366 0.05389790 -0.4803437 -0.9795159 8
## 4: 0.9653366 0.05389790 -0.4803437 -0.9795159 8
## 5: 0.9653366 0.07808923 -0.7459677 -1.0018490 1
## 6: 0.9653366 0.06553210 -0.6573884 -0.9718021 2
## leadingEdge
## 1: 2547
## 2: 6385
## 3: 5687,5683,7415,10213,5696,5692
## 4: 5687,5683,7415,10213,5696,5692
## 5: 5575
## 6: 1432## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.9883495
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.7300971
## 3: ABC transporter disorders 0.9818781
## 4: ABC-family proteins mediated transport 0.9818781
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.3340122
## 6: ADP signalling through P2Y purinoceptor 1 0.6293103
## padj log2err ES NES size
## 1: 0.9941690 0.04486451 -0.5020161 -0.6775977 1
## 2: 0.9789094 0.05884382 -0.6330645 -0.8544806 1
## 3: 0.9941690 0.03761426 -0.2167689 -0.4513799 8
## 4: 0.9941690 0.03761426 -0.2167689 -0.4513799 8
## 5: 0.9361785 0.10319747 0.8366935 1.1263557 1
## 6: 0.9361785 0.07113274 0.5955884 0.9113494 2
## leadingEdge
## 1: 2547
## 2: 6385
## 3: 5696,7415,5687,5692,5683,10213,...
## 4: 5696,7415,5687,5692,5683,10213,...
## 5: 5575
## 6: 6714
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.## pathway pval
## 1: 2-LTR circle formation 0.8011696
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.5107212
## 3: ABC transporter disorders 0.7048458
## 4: ABC-family proteins mediated transport 0.7048458
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.2865497
## 6: ADP signalling through P2Y purinoceptor 1 0.7500000
## padj log2err ES NES size
## 1: 0.9529068 0.05468085 0.6028226 0.8076988 1
## 2: 0.8661945 0.07667469 0.7439516 0.9967922 1
## 3: 0.8934204 0.10473282 0.3190184 0.8182498 8
## 4: 0.8934204 0.10473282 0.3190184 0.8182498 8
## 5: 0.7166461 0.11012226 0.8608871 1.1534695 1
## 6: 0.9222656 0.06508776 0.4956529 0.7826204 2
## leadingEdge
## 1: 2547
## 2: 6385
## 3: 5683,5691,5692,10213,5706,5696,...
## 4: 5683,5691,5692,10213,5706,5696,...
## 5: 5575
## 6: 6714## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.75855131
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.79051383
## 3: ABC transporter disorders 0.24154589
## 4: ABC-family proteins mediated transport 0.24154589
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.07905138
## 6: ADP signalling through P2Y purinoceptor 1 0.27563025
## padj log2err ES NES size
## 1: 0.9129258 0.05871859 -0.6310484 -0.8313235 1
## 2: 0.9137781 0.05594286 0.5967742 0.8107522 1
## 3: 0.6504293 0.09139243 0.5582638 1.1868006 8
## 4: 0.6504293 0.09139243 0.5582638 1.1868006 8
## 5: 0.5667416 0.22496609 0.9616935 1.3065162 1
## 6: 0.6504293 0.10319747 0.7676768 1.1662680 2
## leadingEdge
## 1: 2547
## 2: 6385
## 3: 5683,5687,10213,5692,7415,5691
## 4: 5683,5687,10213,5692,7415,5691
## 5: 5575
## 6: 1432,6714
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.7137255
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.1509804
## 3: ABC transporter disorders 0.7577093
## 4: ABC-family proteins mediated transport 0.7577093
## 5: ADORA2B mediated anti-inflammatory cytokines production 0.7274510
## 6: ADP signalling through P2Y purinoceptor 1 0.1886477
## padj log2err ES NES size leadingEdge
## 1: 0.9779589 0.06037864 0.6350806 0.8540177 1 2547
## 2: 0.6337330 0.15851411 0.9274194 1.2471370 1 6385
## 3: 0.9779589 0.10027911 0.2658487 0.7583154 8 5696,5683
## 4: 0.9779589 0.10027911 0.2658487 0.7583154 8 5696,5683
## 5: 0.9779589 0.05947603 0.6270161 0.8431731 1 5575
## 6: 0.7175283 0.12814292 -0.7711795 -1.2147540 2 1432## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
#data_results <- get_df_long(dep)Serine/Threonine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: 2-LTR circle formation 0.007713272
## 2: ABC transporter disorders 0.630434783
## 3: ABC-family proteins mediated transport 0.569264069
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.048096663
## 5: ADP signalling through P2Y purinoceptor 1 0.964426877
## 6: AKT phosphorylates targets in the cytosol 0.098148148
## padj log2err ES NES size leadingEdge
## 1: 0.6413673 0.40701792 0.9972973 1.3250367 1 3159
## 2: 0.9889853 0.06674261 -0.6810811 -0.9197246 1 5684
## 3: 0.9889853 0.07647671 0.6147864 0.9529845 2 23,5684
## 4: 0.6413673 0.32177592 0.8144864 1.4475812 3 5576,5577,5573
## 5: 0.9933804 0.04678830 -0.5162162 -0.6970929 1 5321
## 6: 0.7814707 0.19381330 -0.8827569 -1.3271503 2 84335data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: 2-LTR circle formation 0.005040512
## 2: ABC transporter disorders 0.545267490
## 3: ABC-family proteins mediated transport 0.892778993
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.197560976
## 5: ADP signalling through P2Y purinoceptor 1 0.299227799
## 6: AKT phosphorylates targets in the cytosol 0.084403670
## padj log2err ES NES size leadingEdge
## 1: 0.5466665 0.40701792 0.9972973 1.3386123 1 3159
## 2: 0.9960152 0.07608372 -0.7162162 -0.9648626 1 5684
## 3: 0.9978247 0.05502111 0.4139129 0.6440246 2 23
## 4: 0.9960152 0.15419097 0.7360016 1.3030981 3 5576,5577,5573
## 5: 0.9960152 0.10672988 0.8554054 1.1481593 1 5321
## 6: 0.9960152 0.20895503 -0.8899015 -1.3279377 2 84335
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.02087899 0.8251450
## 2: ABC transporter disorders 0.64624506 0.9848593
## 3: ABC-family proteins mediated transport 0.77828746 0.9848593
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.03051579 0.8251450
## 5: ADP signalling through P2Y purinoceptor 1 0.20481928 0.9848593
## 6: AKT phosphorylates targets in the cytosol 0.20030581 0.9848593
## log2err ES NES size leadingEdge
## 1: 0.35248786 0.9918919 1.3367314 1 3159
## 2: 0.06553210 -0.6810811 -0.9096888 1 5684
## 3: 0.04486451 -0.5331529 -0.8011377 2 5684,23
## 4: 0.35248786 0.8550136 1.6044105 3 5576,5577,5573
## 5: 0.13574094 0.8959459 1.2074290 1 5321
## 6: 0.11776579 -0.8099516 -1.2170670 2 84335EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.1768173 0.7943790
## 2: ABC transporter disorders 0.9194499 0.9754768
## 3: ABC-family proteins mediated transport 0.3558648 0.8561713
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.7941748 0.9673784
## 5: ADP signalling through P2Y purinoceptor 1 0.1967546 0.7943790
## 6: AKT phosphorylates targets in the cytosol 0.9502982 0.9841196
## log2err ES NES size leadingEdge
## 1: 0.14551615 -0.9081081 -1.2240437 1 3159
## 2: 0.04870109 -0.5445946 -0.7340619 1 5684
## 3: 0.09787733 -0.7175666 -1.1003535 2 23
## 4: 0.05490737 -0.4599280 -0.7839573 3 5576
## 5: 0.13959967 0.9054054 1.2137234 1 5321
## 6: 0.04773424 -0.4130288 -0.6333596 2 572#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval padj
## 1: 2-LTR circle formation 0.07676768 0.9608552
## 2: ABC transporter disorders 0.61493124 0.9608552
## 3: ABC-family proteins mediated transport 0.39509954 0.9608552
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.70250896 0.9608552
## 5: ADP signalling through P2Y purinoceptor 1 0.66797642 0.9608552
## 6: AKT phosphorylates targets in the cytosol 0.38968481 0.9608552
## log2err ES NES size leadingEdge
## 1: 0.23112671 -0.9648649 -1.2916120 1 3159
## 2: 0.06767604 0.6837838 0.9198979 1 5684
## 3: 0.07747675 -0.6551541 -1.0665815 2 23
## 4: 0.09255289 0.3980628 0.8110229 3 5573,5577
## 5: 0.06364241 0.6581081 0.8853563 1 5321
## 6: 0.11524000 0.6129905 1.0457381 2 572,84335#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.1 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.0.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.5
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.39
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.21.1 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.0.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()